Seurat_Code_Filtering_2020

R Markdown

#install.packages("BiocManager")
#BiocManager::install("Seurat")
#BiocManager::install("ggplot2")
#install.packages("dplr")
#install.packages("ape")
#install.packages("cowplot")
#install.packages("Matrix")

Load packages into your workspace.

library(Seurat)
library(ggplot2)
library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

library(ape)
library(cowplot)
library(Matrix)

#find out where you are
getwd()

## [1] "/Users/whippoorwill/Desktop/Code/scrnaseq/Rmd"

Edit the following code every time and make sure the folders “QC” and … are present in the “dir” folder

#Specify your preferred directory for all input + output
dir= "/Users/whippoorwill/Desktop/Sequencing/LD_AVM02"

#Specify exactly where your seurat files live
datafolder = "Data/Seurat"

#Specify your organism; please capitalize the first letter (i.e. "Mouse", "Human","Zebrafish","Rat")
organism = "Mouse"

#metrics you want to look at for QC
m = c("nCount_RNA","nFeature_RNA","percent.mito","percent.ribo")

#set up folders
QCfolder = "QC"

#This name needs to match your project name within the seurat object
project<-"Microglia_BC" 

#You can add in housekeeping genes if you want them to be scaled always; otherwise set to "NULL"
add.genes = NULL

#Choose what to regress out - could be age, sex, or any metadata column
regress = c("nFeature_RNA")

#Decide whether to use SCtransform (can take a long time with big datasets; generally recommended)
sct = TRUE

#How many genes do you want scaled to use for UMAP/clustering? 
ngenes = 10000

#Which principal components do you want to calculate on? This is a default setting, change if one of the pc's is for something you think is a technical error (i.e. HSP, RP, etc)
pcs = 1:30

#clustering resolution; the last number will be saved as "seurat_clusters" in metadata
res = c(0.5,1.5,1.0)

#Important genes to determine your cells of interest
igenes = c("Cx3cr1","Rbfox3","Aldh1l1","Pecam1")

#metadata dimensions you want to cluster on
dims = c("seurat_clusters","sample_description","age","condition")

#edit to include all desired celltypes to subset on later
keep = c("Macrophages")

#make a unique name; maybe this is the celltype you've already subset, or the age you're looking at, etc. 
iterationname = "Allcells"

Load in your filtered dataset

load(file.path(dir,datafolder,paste0(project,"_filtered.RData")))

Function to print multiple graphs:

PrintSeuratGraph = function(namecard = "a",seurat_object = sobject,graphtype = "feature",feature = NULL,group = NULL,split=NULL,cellnames=NULL){
  if (!is.null(cellnames)){
    Idents(seurat_object) = cellnames[1]
    cells = colnames(seurat_object)[Idents(seurat_object) %in% cellnames[2:length(cellnames)]]} 
  else {cells = cellnames}
  if (graphtype == "feature"){
    graph = FeaturePlot(seurat_object,features = feature,split.by = split, cells = cells,cols = c("lightyellow","darkred"))
  }
  if (graphtype == "violin"){
    graph = VlnPlot(seurat_object,features = feature, pt.size = 0.1, idents = cellnames[2:length(cellnames)],group.by = group, split.by = split)
  }
  if (graphtype == "dim"){
    graph = DimPlot(seurat_object,cells = cells, group.by = group, split.by = split)
    
  }
  name = paste0(feature,"_",graphtype,namecard,".eps")
  graph
  setEPS()
  postscript(file.path(dir,QCfolder,name))
  print(graph)
  dev.off()
}

Find variable features, normalize, scale, run PCA, clustering, umap The following is the standard method of normalization and scaling. Interchangeable with the next chunk. Run both, you should have already specified which to use with “sct = T or F”. Will take 5-20 minutes to run.

if (!sct){
  sobject <- NormalizeData(sobject,normalization.method = "LogNormalize", scale.factor = 10000)
  sobject<-FindVariableFeatures(sobject, selection.method = "vst", nfeatures = ngenes)
  
  all.genes<-rownames(sobject)
  var.genes = VariableFeatures(sobject)
  add.genes = add.genes[!add.genes %in% var.genes]
  
  any(add.genes %in% var.genes)
  scalegenes = c(var.genes,add.genes)
  VariableFeatures(sobject) = scalegenes
  sobject<-ScaleData(sobject,features = VariableFeatures(sobject), vars.to.regress = regress)
}

Alternative: SCTransform (great for smaller datasets)

if (sct){
  sobject <- SCTransform(sobject, vars.to.regress = regress, verbose = FALSE,variable.features.n = ngenes,conserve.memory = T)
}

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## Warning in sqrt(1/i): NaNs produced

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## Warning in vst(umi = new("dgCMatrix", i = c(5L, 8L, 11L, 14L, 25L, 29L, : Will
## not return corrected UMI because residual type is not set to 'pearson'

## Calculating variance for residuals of type pearson for 18794 genes

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## Calculating residuals of type pearson for 10000 genes

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Show most variable genes

labels <- c(head(VariableFeatures(sobject),10),add.genes)
plot1 = VariableFeaturePlot(sobject)
LabelPoints(plot=plot1, points = labels, repel = F, xnudge = 0.1, ynudge = 0.5)

## Warning: Using `as.character()` on a quosure is deprecated as of rlang 0.3.0.
## Please use `as_label()` or `as_name()` instead.
## This warning is displayed once per session.

Run PCA analysis and show elbow plot

sobject <- RunPCA(sobject,features = VariableFeatures(sobject),npcs = 50, verbose = FALSE)
ElbowPlot(sobject,ndims = 50, reduction = "pca")

print(sobject[["pca"]], dims = 1:20, nfeatures = 5)

## PC_ 1 
## Positive:  Ctsd, Hexb, Ctsb, Mki67, C1qb 
## Negative:  Ptprz1, Ptn, Fabp7, Gria2, Nedd4 
## PC_ 2 
## Positive:  Apoe, Ctsd, Ftl1, Ctsb, Lyz2 
## Negative:  Mki67, Hist1h1b, Top2a, Hist1h2ap, Hist1h2ae 
## PC_ 3 
## Positive:  Map1b, Meg3, Gm42418, Snhg11, Elavl3 
## Negative:  Slc1a3, Apoe, Aqp4, Pla2g7, Gja1 
## PC_ 4 
## Positive:  Meg3, Gm42418, Snhg11, Arpp21, Pfkp 
## Negative:  Sox11, Dlx1, Nnat, Ccnd2, Dlx2 
## PC_ 5 
## Positive:  Lhfpl3, Olig2, Olig1, Sox10, C1ql1 
## Negative:  Apoe, Igf1, Fabp5, Lyz2, Spp1 
## PC_ 6 
## Positive:  Atad2, Mcm6, Mcm3, Gm26917, Lig1 
## Negative:  Apoe, Cenpf, Igf1, Ube2c, Mki67 
## PC_ 7 
## Positive:  Hist1h1b, Apoe, Hist1h2ap, Hist1h2ae, Rrm2 
## Negative:  Cenpf, Ube2c, Cenpa, Aspm, Arl6ip1 
## PC_ 8 
## Positive:  Ctsd, Ctsl, Ctsb, Cd9, Hexb 
## Negative:  Ifitm3, Cybb, Pf4, Ahnak, F13a1 
## PC_ 9 
## Positive:  H2afz, Mt1, Tubb4b, Ftl1, Ran 
## Negative:  Apoe, Xist, Malat1, Selenop, Abca1 
## PC_ 10 
## Positive:  Map1b, Mbp, 9630013A20Rik, Plp1, Enpp6 
## Negative:  Pdgfra, Ednrb, Rlbp1, Ptn, Cspg4 
## PC_ 11 
## Positive:  Lgals3, S100a6, Napsa, S100a4, Ifitm3 
## Negative:  Pf4, Mrc1, Ms4a7, Dab2, Lyve1 
## PC_ 12 
## Positive:  Hist1h1b, Hist1h2ae, Apoe, Hist1h2ap, Eef1a1 
## Negative:  Xist, Fabp5, Spp1, Lpl, Cd9 
## PC_ 13 
## Positive:  Apoe, Ascl1, Egfr, Myo10, Dll3 
## Negative:  Stmn2, Nrxn3, Dcx, Ccl3, Dlx6os1 
## PC_ 14 
## Positive:  Isg15, Ifitm3, Ifi204, Bst2, Oasl2 
## Negative:  Ahnak, Vim, S100a6, Crip1, Napsa 
## PC_ 15 
## Positive:  Hist1h2ae, Hist1h1b, Hist1h2ap, Hist1h1e, Hist1h2ab 
## Negative:  Apoe, Cenpf, Aspm, Dtl, Ube2c 
## PC_ 16 
## Positive:  Spp1, Igf1, Gpnmb, Fabp5, Lgals1 
## Negative:  Ccl3, Mt1, Crybb1, Ccl4, Ctsd 
## PC_ 17 
## Positive:  Ctsd, Gria2, Arpp21, Kcnq1ot1, Vcan 
## Negative:  Ccl4, Gap43, Ccl12, Mapt, Ccl2 
## PC_ 18 
## Positive:  Hmgn2, H1fx, Mdk, Insm1, Hist1h2bc 
## Negative:  Xist, Draxin, Malat1, Crlf1, Dbx2 
## PC_ 19 
## Positive:  Apoe, Ccl4, Ccl3, Ccl12, Ccl2 
## Negative:  Ctsd, Xist, Ctsb, Cd63, Syngr1 
## PC_ 20 
## Positive:  Ccl3, Spp1, Csf1, Ccl6, Crybb1 
## Negative:  Apoe, Ctsd, Abca1, Abcg1, Lyz2

Once you are satisfied with pc’s, run clustering:

sobject<-RunUMAP(sobject,reduction = "pca",dims = pcs, verbose = F)

## Warning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
## To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
## This message will be shown once per session

sobject<-FindNeighbors(sobject,dims=pcs,verbose=F)
sobject<-FindClusters(sobject,verbose=F,resolution = res)

Plot important objects; check parameters before moving forward, evaluate QC, clustering

for (dim in dims){
  print(DimPlot(sobject,group.by = dim, label = T))
}

FeaturePlot(sobject,igenes)

FeaturePlot(sobject,m)

VlnPlot(sobject,igenes,sort = "increasing",pt.size = 0.01)

#Build a clustering tree
Idents(sobject) = "seurat_clusters"
sobject= BuildClusterTree(sobject,dims = pcs)
tree = sobject@tools$BuildClusterTree
plot.phylo(tree, use.edge.length = T, direction = "rightwards")

#If you want to see a certain cluster: 
cluster = '13'

#select only the cells from that cluster
cells = sobject$seurat_clusters
cells = cells[cells == cluster]

DimPlot(sobject,cells.highlight = names(cells))

If you are happy with the QC, you can move on to either subdividing further to isolate interesting cells (below) or running differential expression comparisons on the existing clustering.

Note that the chunk below requires you to manually decide which clusters to keep or exclude. The violin plots will help decide based on marker genes. You can also choose to annotate (as many types as you want) and skip subsetting.

#Edit this part carefully. You can add any number of types. Each cluster can only be one type. 
type1 = c(16,17,18,21)
name1 = "other"
type2 = c(0:15,19,20,22) 
name2 = "Macrophages"

#Initialize the cluster levels as a vector and replace the cluster levels with the appropriate name. 
clusters = as.factor(sobject$seurat_clusters)
type = levels(clusters)
type[type1+1] = name1
type[type2+1] = name2
levels(clusters) = type

#Add a metadata column
sobject$celltype = clusters

#check the celltype assignment for accuracy
table(sobject$celltype,sobject$seurat_clusters)

##              
##                  0    1    2    3    4    5    6    7    8    9   10   11   12
##   Macrophages 1310 1299 1285 1255 1093  944  878  872  649  520  489  390  312
##   other          0    0    0    0    0    0    0    0    0    0    0    0    0
##              
##                 13   14   15   16   17   18   19   20   21   22
##   Macrophages  294  257  238    0    0    0  139  126    0   59
##   other          0    0    0  224  204  143    0    0   82    0

#Check them against your marker genes
VlnPlot(sobject,igenes,group.by = "celltype",pt.size = 0.01)

#add a metadata column labelling each cluster
sobject$celltypecluster = paste0(sobject$celltype,"-",sobject$seurat_clusters)

#save the unsubsetted data. 
save(sobject,file = file.path(dir,datafolder,paste0(project,iterationname,"_filtered_labelled_clustered.RData")))

Save the tree

Idents(sobject) = "celltypecluster"
sobject= BuildClusterTree(sobject,dims = pcs)
tree = sobject@tools$BuildClusterTree
setEPS()
postscript(file.path(dir,QCfolder,paste0(project,"_tree_",iterationname,".eps")))
plot.phylo(tree, use.edge.length = T, direction = "rightwards")
dev.off()

## quartz_off_screen 
##                 2

Block to print multiple graphs:

name = paste0(project,iterationname)
genes = igenes
features = m
groups = c(dims,"celltype","celltypecluster")
genes = genes[genes %in% rownames(GetAssayData(sobject,slot = "data"))]

for(feature in genes){
  PrintSeuratGraph(namecard = name,graphtype = "feature",feature = feature)
}

for(feature in features){
  PrintSeuratGraph(namecard = name,graphtype = "feature",feature = feature)
}

#split feature plots by individual
for(feature in c(features)){
  PrintSeuratGraph(namecard = paste0(name,"_split"),graphtype = "feature",feature = feature,split = "sample_description")
}

#dim plots for clustering
for(group in groups){
  PrintSeuratGraph(namecard = name,graphtype = "dim",group = group, feature = group)
}

#violin plots
for(feature in c(genes,features)){
  PrintSeuratGraph(namecard = name,graphtype = "violin",feature = feature,group = "seurat_clusters")
}

Subset the data

Subset the data to include only your celltypes of interest:

#subset the object by the metadata column "celltype"
sobject = subset(sobject,subset = celltype %in% keep)

#check the resulting subset
table(sobject$celltype,sobject$seurat_clusters)

##              
##                  0    1    2    3    4    5    6    7    8    9   10   11   12
##   Macrophages 1310 1299 1285 1255 1093  944  878  872  649  520  489  390  312
##   other          0    0    0    0    0    0    0    0    0    0    0    0    0
##              
##                 13   14   15   16   17   18   19   20   21   22
##   Macrophages  294  257  238    0    0    0  139  126    0   59
##   other          0    0    0    0    0    0    0    0    0    0

Save the filtered, subset data:

save(sobject,file = file.path(dir,datafolder,paste0(project,"_",iterationname,"_",keep,"_subset.RData")))